论文标题
一种基于聚类匹配的视频面部识别方法
A Cluster-Matching-Based Method for Video Face Recognition
论文作者
论文摘要
在我们的日常生活中，许多现代解决方案和数千种应用中都存在面部识别系统。但是，当前的解决方案不容易扩展，尤其是在增加新目标人士时。我们在视频中提出了一种基于聚类匹配的方法来识别面部识别方法。在我们的方法中，我们使用无监督的学习来聚集数据集中的面孔和选择的视频中的面孔。此外，我们设计了一个匹配启发式的群集，以将两组中的群集关联在一起，该集群也能够识别何时面部属于未注册的人。在视频面部识别任务中，我们的方法的召回率为99.435％，精度为99.131％。除了执行面部识别外，还可以用于确定每个人在场的视频片段。
Face recognition systems are present in many modern solutions and thousands of applications in our daily lives. However, current solutions are not easily scalable, especially when it comes to the addition of new targeted people. We propose a cluster-matching-based approach for face recognition in video. In our approach, we use unsupervised learning to cluster the faces present in both the dataset and targeted videos selected for face recognition. Moreover, we design a cluster matching heuristic to associate clusters in both sets that is also capable of identifying when a face belongs to a non-registered person. Our method has achieved a recall of 99.435% and a precision of 99.131% in the task of video face recognition. Besides performing face recognition, it can also be used to determine the video segments where each person is present.
